Recent advances in imaging techniques have enabled more rapid and higher-resolution volumetric imaging in a variety of applications, such as medical imaging. Image data may be processed electronically to facilitate various visualizations. One such task is segmentation, in which areas of an image are classified as being a member of a given class or not. For example, each brain MRI includes a hippocampus, and a segmentation task may identify the portion of the MRI corresponding to the hippocampus.
Another type of task that may be applied to volumetric imaging is anomaly detection. Anomaly detection tasks typically seek to determine a probability that a given 3D image belongs to a class, such as presence of a disease, anomaly or other condition of interest, where only some volumetric images are expected to exhibit the condition. However, to date, analysis of such images to identify anomalous conditions has largely relied on human analysis and expertise.
Moreover, individuals trained to review medical images, such as radiologists, tend to be highly skilled and highly paid. Using current approaches, their work may involve exhaustive manual review of potentially hundreds of image slices. As imaging systems are used with increased frequency, to generate increasing volumes of image data, the cost of traditional radiologist review may be burdensome. A limited supply of qualified radiologists analyzing an increasing volume of medical image data may lead to critical delays in analysis results, and possible negative impacts on the precision and recall of analysis results.
Computer aided detection and diagnosis (CAD) technologies have been used to facilitate diagnostic use of medical imaging data. Such technologies often rely on traditional image processing approaches, and are typically based on handcrafted, problem-specific heuristics and features. The use of these approaches has been hampered by high processing times, inaccuracy and non-generalizability. These issues may be exacerbated by rapidly rising data volumes generated by current high-resolution volumetric imaging devices. As a result, CAD technologies have become common for use only in a few specific applications, such as mammography.